Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been st...Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.展开更多
With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,at...With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,attack planning under uncertainty is more suitable and practical for pentesting than is the traditional planning approach,but it also poses some challenges.To address the efficiency problem in uncertainty planning,we propose the APU-D*Lite algorithm in this paper.First,the pentest framework is mapped to the planning problem with the Planning Domain Definition Language(PDDL).Next,we develop the pentest information graph to organize network information and assess relevant exploitation actions,which helps to simplify the problem scale.Then,the APU-D*Lite algorithm is introduced based on the idea of incremental heuristic searching.This method plans for both hosts and actions,which meets the requirements of pentesting.With the pentest information graph as the input,the output is an alternating host and action sequence.In experiments,we use the attack success rate to represent the uncertainty level of the environment.The result shows that APU-D*Lite displays better reliability and efficiency than classical planning algorithms at different attack success rates.展开更多
基金the National Natural Science Foundation of China No.61502528.
文摘Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.
文摘With serious cybersecurity situations and frequent network attacks,the demands for automated pentests continue to increase,and the key issue lies in attack planning.Considering the limited viewpoint of the attacker,attack planning under uncertainty is more suitable and practical for pentesting than is the traditional planning approach,but it also poses some challenges.To address the efficiency problem in uncertainty planning,we propose the APU-D*Lite algorithm in this paper.First,the pentest framework is mapped to the planning problem with the Planning Domain Definition Language(PDDL).Next,we develop the pentest information graph to organize network information and assess relevant exploitation actions,which helps to simplify the problem scale.Then,the APU-D*Lite algorithm is introduced based on the idea of incremental heuristic searching.This method plans for both hosts and actions,which meets the requirements of pentesting.With the pentest information graph as the input,the output is an alternating host and action sequence.In experiments,we use the attack success rate to represent the uncertainty level of the environment.The result shows that APU-D*Lite displays better reliability and efficiency than classical planning algorithms at different attack success rates.
